A spatiotemporal data dynamic standardization access method, device and readable storage medium

Abstract

This invention discloses a method, apparatus, and readable storage medium for dynamic standardized access to spatiotemporal data, relating to the field of geographic information technology. The method recursively traverses multi-source heterogeneous spatiotemporal data packets, parses the physical directory structure, and constructs a hierarchical JSON template mapping logical relationships. Based on this template, it dynamically configures four-level cascading quality inspection rules covering directory integrity, layer metadata, geometric topology, and attribute specifications. A state machine mechanism drives the rule engine to perform automated verification, and based on the verification results, it achieves structured storage of vector data and object-oriented archiving of non-vector data. This invention decouples business logic from underlying code through template construction and rule configuration, significantly reduces invalid computations using a state machine circuit breaker mechanism, and effectively solves the technical problems of poor flexibility, low scalability, and inconsistent standards in the spatiotemporal data access process.

Description

Technical Field

[0001] This invention relates to the fields of geographic information systems and data governance technology, specifically to a method, apparatus, and readable storage medium for dynamic standardized access to spatiotemporal data. Background Technology

[0002] With the deepening development of digital twin cities, natural resource surveys, and land spatial planning, the aggregation and management of spatiotemporal data has become a key aspect of new infrastructure construction. Spatiotemporal data is characterized by its multi-source, heterogeneous, and complex structure, encompassing various forms such as vector data (e.g., Shapefile, GeoJSON), raster imagery (e.g., TIFF), 3D models, and unstructured documents. To achieve unified storage and service publishing of these massive amounts of data, a series of processing steps are typically required, including data parsing, quality checks, format conversion, and storage.

[0003] In the existing technological system, the following are the main technical solutions for accessing and processing spatiotemporal data:

[0004] The first type is the customized script development model. This is the most common approach in current engineering practice, where developers write dedicated Python scripts or Java programs for hard-coded parsing for each specific data submission project. While this approach is highly targeted, it has extremely low reusability and is very insensitive to changes in the data directory structure.

[0005] The second category is dedicated data engines for specific fields. For example, Chinese patent application CN106897954A discloses a smart city spatiotemporal information cloud platform. This solution mainly targets sensor data (such as video streams and BeiDou positioning data), and achieves data storage through preset receiving, type setting, and preprocessing modules. However, this existing technology mainly focuses on the processing of streaming data, and its processing flow is relatively fixed (receiving-preprocessing-storage). It lacks the ability to recursively parse complex nested directory structures and is difficult to adapt to the variable physical storage structure of file-based spatiotemporal data packets.

[0006] The third category is parsing methods based on metadata configuration. For example, Chinese patent application CN113590894A discloses a dynamic and efficient method for importing and retrieving remote sensing image metadata. This scheme obtains node information through XML parsing and defines parsing rules using configuration files. Although this technology improves adaptability to new types of remote sensing images to some extent, it still relies on field mapping of specific metadata files (such as XML) and lacks the ability to deeply verify the geometric topology and spatial reference system of the data entities themselves.

[0007] Furthermore, in the general field of data governance, there are rule-engine-based ETL tools (such as the rule writing system mentioned in US Patent US10346139B2). While these tools allow transformation rules to be defined via a GUI, they typically lack support for logic specific to the GIS domain. For example, they struggle to handle "multi-file aggregation" logic, such as Shapefiles, which consist of multiple files (.shp, .shx, .dbf), and cannot directly perform complex spatial geometric self-intersection or overlap checks.

[0008] In summary, existing technologies still have the following unresolved shortcomings when facing the demand for large-scale, structurally diverse spatiotemporal data access:

[0009] Physical structure and logical parsing are tightly coupled: Current technologies lack a universal method to shield differences in physical file systems. Once the directory hierarchy or naming conventions of upstream data are slightly adjusted, the parsing code often needs to be rebuilt, resulting in poor system robustness.

[0010] The quality inspection rules lack flexibility: The quality inspection logic of existing technologies (such as coordinate system verification and topology inspection) is usually fixed inside the program or limited to simple field mapping, which cannot support business personnel to dynamically configure multi-level (directory-layer-geometry-attribute) cascading quality inspection rules by "building blocks".

[0011] Lack of dynamic circuit breaker mechanism: When processing massive data packets, existing technologies often use full scan or linear processing, which leads to a large waste of computing resources when encountering data with incorrect format.

[0012] Therefore, there is an urgent need for a spatiotemporal data access method that can decouple physical storage from business logic, support dynamic rule configuration, and have efficient cascading quality inspection capabilities. Summary of the Invention

[0013] In view of the above-mentioned problems, the present invention is proposed.

[0014] The purpose of this invention is to overcome the above-mentioned problems and provide a method, apparatus and readable storage medium for dynamic standardized access to spatiotemporal data, so as to solve the technical problems existing in the background art.

[0015] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0016] Firstly, a method for dynamically standardized access to spatiotemporal data is provided, including the following steps:

[0017] Step S1, Data packet structure parsing: Recursively traverse the received spatiotemporal data packets, parse the directory node structure, identify the node types, and extract the metadata information of the spatial data layer; the node types include at least folders, text, images, and spatial data layers;

[0018] Step S2, Template Construction: Based on parsing the directory node structure and extracting metadata information, a hierarchical spatiotemporal data template is constructed. The template maps the logical structure of the spatiotemporal data packet in a structured data format.

[0019] Step S3, rule configuration: Based on the spatiotemporal data template, configure a multi-level quality inspection rule base; the multi-level quality inspection rule base shall at least cover the verification rules of four dimensions: directory structure, layer information, geometric features and attribute fields;

[0020] Step S4, Cascaded Quality Inspection and Storage: Using the rule engine, the rules in the multi-level quality inspection rule base are executed according to the preset cascaded control logic to verify the spatiotemporal data packet; when the verification passes, the data is split and written to the corresponding storage system according to the data type.

[0021] Further, in step S1, the extraction of metadata information of the spatial data layer includes: identifying spatial data files using a geospatial data processing library, aggregating related files with the same filename but different suffixes into a single logical layer node; and extracting the coordinate reference system, geometric type, spatial extent, time range, and attribute table structure information of the logical layer node.

[0022] Furthermore, in step S2, the spatiotemporal data template adopts JSON format; the node objects in the template include path, name, and type fields; for nodes of type spatial data layer, the template also includes geometry type field, coordinate system field, spatiotemporal range field, and attribute list field.

[0023] Further, in step S3, the multi-level quality inspection rule base specifically includes: Level 1 rules, used to verify the naming standardization of directories and files, the correctness of node types and structural integrity, and the configuration verification of file content; Level 2 rules, used to verify coordinate system consistency, raster resolution consistency, and band consistency; Level 3 rules, used to verify geometric type consistency and spatial topological relationships, including self-intersection, multi-component, and overlap checks; and Level 4 rules, used to verify the name, type, length, value range, and non-empty constraints of attribute fields.

[0024] Furthermore, in step S4, the cascading control logic is implemented based on a state machine mechanism: a data processing state machine is constructed, including a directory check state, a layer check state, a geometry check state, and an attribute check state; the state machine executes the checks sequentially according to the first to fourth level rules; only when all rule checks in the current state pass, the state transition is triggered to the next check state; if any state check fails, the process is terminated and an error report is output.

[0025] Furthermore, in step S1, a breadth-first search algorithm is used to traverse the spatiotemporal data packet, and the nodes generated by the traversal are stored as node triples containing path, name, and type.

[0026] Further, in step S4, the process of splitting and writing the data into the corresponding storage system includes: identifying the data type; if it is vector data, then dynamically creating or matching a data table in the relational database according to the attribute table structure information in the spatiotemporal data template, and storing the geometric information after converting it into a spatial format; if it is raster data or non-spatial data, then writing its file entity into the object storage system, and recording the file's metadata information and storage path in the relational database.

[0027] In a second aspect, a computer device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method of any one of the first aspects.

[0028] Thirdly, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the method of any one of the first aspects.

[0029] The beneficial effects of this invention are as follows: By constructing a closed-loop collaborative mechanism of "heterogeneous parsing - intermediate state mapping - cascaded quality inspection," this invention fundamentally solves the technical challenge of tight coupling between business rules and underlying code in spatiotemporal data access. First, by combining recursive directory parsing technology with hierarchical JSON template construction, an "intermediate logic adaptation layer" independent of physical storage is established. This template abstracts messy physical files (such as multiple file combinations of Shapefiles and raster images) into unified logical nodes, allowing subsequent quality inspection rules to be defined based on standardized JSON objects. This shields the heterogeneity of the underlying file formats and achieves deep decoupling between the data physical structure and business verification logic. Second, the configurable rule engine and state machine control mechanism achieve dynamic collaboration through "content and process separation": the rule engine is responsible for defining specific verification indicators (such as regular expression matching and topology checks), while the state machine is responsible for orchestrating the execution order of these indicators, constructing a four-level cascaded circuit breaker system from directory structure and layer metadata to geometric topology and attribute fields. This collaborative mechanism leverages the flow control capabilities of state machines to force the system to follow an execution logic that progresses "from surface to core, from lightweight to heavyweight"—that is, only after the low-overhead directory structure verification (level 1 state) passes will the high-resource-consuming GDAL / JTS library be invoked for spatial computation (level 3 state). This mechanism not only significantly improves the system's adaptability to new data standards through "zero-code" configuration, but also avoids deep computation on invalid data through an automatic circuit breaker strategy between states. While ensuring the rigor of the data entering the database, it significantly reduces the server's I / O load and computing resource consumption. Attached Figure Description

[0030] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 A standardized JSON index file structure adapted to the generation system of the present invention method;

[0032] Figure 2 This is a flowchart of Embodiment 1 of the method of the present invention;

[0033] Figure 3 This is a structural block diagram of the apparatus according to Embodiment 2 of the present invention;

[0034] Figure 4 This is a structural block diagram of a readable storage medium according to Embodiment 2 of the present invention. Detailed Implementation

[0035] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0036] Example 1:

[0037] refer to Figure 2 This embodiment proposes a method for dynamic standardized access to spatiotemporal data, specifically including:

[0038] Step 1: Spatiotemporal Data Packet Structure Analysis

[0039] 1. Spatiotemporal data package creation: Users can create highly customized spatiotemporal data packages by freely combining folders, databases, layers, text, and images according to specific business scenarios and application requirements; among them, layers are spatiotemporal data, including vector data and raster data;

[0040] 2. Spatiotemporal Data Packet Parsing: When a user uploads a spatiotemporal data packet, Path.walkFileTree is used to recursively traverse the packet, parsing its directory nodes. When a node's attribute is spatiotemporal data, GDAL is used to parse the node's metadata and attribute fields. The specific steps for spatiotemporal data packet parsing are as follows:

[0041] 2.1 Directory Node Parsing: Receive the uploaded spatiotemporal data packet and decompress it to a temporary working directory; based on a breadth-first search algorithm, recursively traverse the root node, child nodes, and leaf nodes of the temporary working directory; during this process, record the path, name, and type of each node; the specific steps are as follows:

[0042] 2.1.1 Directory Structure Definition: The directory structure is a rooted tree. ,in:

[0043] (1) A collection of all nodes, consisting of folders, databases, layers, text, or images;

[0044] (2) Indicates a father-son relationship;

[0045] (3) The path to the root node;

[0046] 2.1.2 Directory Node Resolution: A breadth-first search algorithm is used, as follows:

[0047] Set up a queue Initially The set of visited nodes .

[0048] At step t (t=0,1,2,3,…), execute:

[0049] (1) If Using queue functions Retrieve the current node read in step t. :

[0050]

[0051] (2) Read the current node Add to the set of nodes before step t This yields the set of nodes up to step t+1. :

[0052]

[0053] (3) Read all child nodes of the current node set :

[0054]

[0055] (4) Set the node set before step t+1. The set of all child nodes of the current node Add to the same queue:

[0056]

[0057] when When the algorithm terminates, at this time...

[0058] 2.2 Directory Node Storage: For any node Calculate and store the triples as follows:

[0059]

[0060] In the formula, For the current node, This represents the path from node r to v; For node name, It is in node format, specifically:

[0061]

[0062] In the formula, when node When the file extension is txt, word, pdf, xml or xlsx, the node format is... for (Text); when node Node format when using shp, kml, kmz, or geojson For geometry (vector); when the node Node format when the format is tiff, img, png or jpg For image; when node for At that time, node format for (Folder);

[0063] 2.3 Spatial Data Layer Node Parsing: Based on 2.2, spatial data layer identification is performed. The specific operations are as follows:

[0064] 2.3.1 File Type Recognition: Based on the file extension, identify the main body of all spatial data files, including but not limited to: vector data files Shapefile (.shp), GeoJSON (.geojson), Keyhole Markup Language (.kml / .kmz), and raster data files (.tiff / .img);

[0065] 2.3.2 Layer Component Association: For layer formats defined by multiple files, layer components are aggregated based on their prefix names. Taking Shapefile format as an example, files with the same prefix such as .shp, .shx, .dbf, and .prj are associated and aggregated into a single logical spatial data layer.

[0066] 2.3.3 Layer Metadata Parsing: For each spatial data layer identified in 2.3.2, its metadata information is parsed using the GDAL API. The spatial data layer metadata information includes at least the following:

[0067] (1) Coordinate system: By parsing the coordinate system information inside the relevant projection file prj or data, the description of its spatial reference system is obtained;

[0068] (2) Geometry type: By reading the spatial data header file or content, the geometry type of the layer is automatically identified and labeled as point, line, surface, or raster;

[0069] (3) Spatial range: Directly read or calculate the spatial range of the layer, i.e. the minimum bounding rectangle, expressed in coordinate pairs (minX, minY, maxX, maxY), where minX represents the minimum coordinate of the X-axis, minY represents the minimum coordinate of the Y-axis, maxX represents the maximum coordinate of the X-axis, and maxY represents the maximum coordinate of the Y-axis.

[0070] (4) Time range: If the data contains time dimension information, the time range represented by the data is parsed from the metadata file or attribute field.

[0071] 2.3.4 Layer Attribute Field Parsing: For each spatial data layer identified in 2.3.3, the data source is read through the GDAL API, its attribute table structure is parsed, and a detailed description of the attribute fields is generated, including:

[0072] (1) Field definition information: including field name, field type, field length, and whether it is required;

[0073] (2) Field value statistics and semantic inference: Sampling statistics are performed on field values ​​to obtain the number of unique values, maximum value, minimum value, and number of null values;

[0074] The spatial data layer recognition algorithm is as follows:

[0075] For any spatial data layer node Calculate and store tuples:

[0076]

[0077] in:

[0078] From the root node r to the layer node The only path;

[0079] For node names;

[0080] , representing a node The type is a vector data file or image;

[0081] For the spatial reference frame of the node;

[0082] This indicates that the feature type is either a point feature, a line feature, a polygon feature, or a raster.

[0083] The node space range is represented as [minX, minY, maxX, maxY], specifically:

[0084] ;

[0085] This refers to the time dimension information of the layer.

[0086] This refers to layer attribute field information;

[0087] 2.4 Structured Representation of Directory Collections: The directory nodes parsed in 2.2 and the spatial data layer nodes parsed in 2.3 are merged to construct a directory mapping:

[0088]

[0089] In the formula, D is the complete and serializable set of directory nodes of the spatiotemporal data packet physical storage structure;

[0090] Step 2: Spatiotemporal Data Template Construction

[0091] Based on the set of directory nodes parsed in step two, a hierarchical directory tree structure represented in JSON format is constructed, which is a spatiotemporal data template; each node is clearly labeled as a folder, database, layer, text, or image, and its path, coordinate system, geometric type, spatial range, time range, and attribute fields are recorded.

[0092] 2.5 Hierarchical directory tree structure: based on the directory node data set in 2.4 Based on this, a JSON tree is recursively assembled to fully reconstruct the spatiotemporal data packet organization logic; each node contains at least the core fields path, name, and type. When the node data attribute is layer data, it should also include geo, crs, space, time, and attr, as detailed below:

[0093] path: The physical and logical path of the node

[0094] name: Node name;

[0095] type: includes folders, databases, layers, text, and images;

[0096] geo: includes points, lines, polygons, and grids;

[0097] crs: The coordinate system field for the spatial layer;

[0098] space: refers to the spatial range where the space layer is located;

[0099] time: refers to the time range of the spatial layer;

[0100] attr: Information about the attribute fields contained in the spatial layer;

[0101] 2.6 Generate a standardized JSON index file adapted to the system: Following the steps above, organize the data into a standardized JSON document adapted to the system and store it in the database. Its structure... Figure 1 As shown.

[0102] Step 3: Custom configuration of spatiotemporal data quality inspection rules

[0103] Based on the spatiotemporal data template constructed in step two and the system-defined quality inspection rule library, specific quality inspection rules are customized according to business needs. Through cascading control, spatiotemporal data package directory structure checks, graphic information checks, geometric element checks, and attribute field checks are realized to ensure the integrity and standardization of data before it is put into the database, and effectively avoid the risks of subsequent applications caused by data quality defects.

[0104] 2.7 Quality Inspection Rule Base: Define a four-level quality inspection rule base, including:

[0105] Level 1 rules: Check the directory structure, including the naming conventions of folders, databases, layers, text, and images; the correctness of node types; the integrity of node structure; and the configuration of file content.

[0106] Secondary rules: Check layer information, including coordinate system consistency verification, resolution consistency verification, and band consistency verification;

[0107] Level 3 rules: Checks geometric features, including: consistency of map data (checks whether the layer geometry type is point, line, or polygon, and whether the number of layer features is correct) and spatial topology (checks whether there is overlap, self-intersection, or multiple parts between layer geometric features).

[0108] Level 4 rules primarily target attribute field checks, including field integrity verification, consistency checks on field name, field type, field length, and whether fields are required.

[0109] 2.8 Customization of Quality Inspection Rules

[0110] Based on the rules in the quality inspection rule base in section 2.7, customize the spatiotemporal data template quality inspection rules as needed, including:

[0111] 2.8.1 Configure Level 1 Quality Inspection Rules;

[0112] (1) Naming convention verification:

[0113] a. Regular expression template matching: Users can configure naming rules for different node types (including folders, databases, layers, text, and images), such as ^[a-z0-9_]+$, supporting global default rules and local overriding;

[0114] b. Multilingual and Special Character Control: Users can configure whether to enable policies such as allowing only ASCII characters, disabling spaces or Chinese characters, and forcing lowercase to prevent cross-platform compatibility issues;

[0115] c. Business semantic prefix and suffix constraints: Users can set custom prefix and suffix constraints for different node types, including folders, databases, layers, text, and images.

[0116] (2) Node type correctness verification:

[0117] Based on dual judgment of file extension and content: User-defined node types include folder, database, layer, text, and image. The system infers the type based on the file extension and verifies it against the user-defined node type.

[0118] (3) Structural integrity verification:

[0119] a. Verify the Shapefile of the layer: Users can choose whether to verify the integrity of the Shapefile and check whether .shp, .shx, .dbf, and .prj files coexist;

[0120] b. Raster data validation: Optionally check if the raster's auxiliary files .ovr, .aux, and .xml exist;

[0121] (4) File content configuration verification:

[0122] a. Whether internal files are redundant: Can the contents of the configuration folder contain redundant files?

[0123] b. Required for the current node: Configures whether the current node must exist;

[0124] c. Name conflict detection: Configure whether files or folders with the same name are allowed in the same parent directory;

[0125] 2.8.2 Configure secondary quality inspection rules;

[0126] (1) Coordinate system consistency verification: Users customize the coordinate system according to business requirements and EPSG coding standards;

[0127] (2) Resolution consistency check: For raster data, set the value of the check resolution;

[0128] (3) Band consistency verification: Set verification bands for raster data;

[0129] 2.8.3 Configure three-level quality inspection rules

[0130] (1) Map consistency verification: The user sets the geometry type to point, line, polygon or raster. The layer is verified to be compliant according to the selected geometry type and the null value of the layer geometry feature attribute is checked by default.

[0131] (2) Topology verification: For vector data, users can configure whether the layers need to be verified by self-intersection, multi-part, and overlay analysis.

[0132] 2.8.4 Configure four-level quality inspection rules;

[0133] (1) Field name and type validation: Set the name and field type for each field. Field types include integer, string, floating point, date, and boolean.

[0134] (2) Field length validation: Set the length limit for each field;

[0135] (3) Field value range validation: Set a separate value range for each field ([minV, maxV] or V), where V refers to a number or a string;

[0136] (4) Field is required: Set whether a field must have a value.

[0137] Step 4: Spatiotemporal data packet quality inspection and database entry

[0138] 2.9 Spatiotemporal Data Packet Quality Inspection

[0139] Users upload new spatiotemporal data packets, which are then matched with the spatiotemporal data template from step two and the corresponding quality inspection rules from step three. The system employs a cascading control mechanism based on the Spring StateMachine framework to build a data state machine, automatically implementing the chained execution of quality inspection rules. That is, after the data packet is uploaded, directory structure checks, layer information checks, geometric feature checks, and attribute field checks are executed sequentially. The entire quality inspection process uses a parallel processing mechanism, which can simultaneously inspect multiple layers, improving processing efficiency. The quality inspection results are returned in structured JSON format, including the problem type and problem description, and a PDF quality inspection report can be generated with one click (as shown in Table 1).

[0140] Table 1 Quality Inspection Report

[0141]

[0142] 2.10 Automated Storage and Archiving of Spatiotemporal Data Packages

[0143] When all quality inspection items pass, the system will trigger a standardized data entry process, classifying and persisting the content of the spatiotemporal data packet to a heterogeneous storage system according to the data type and preset template:

[0144] (1) Vector data (spatial data): written to the PostgreSQL database

[0145] a. Parse vector files (.shp, .geojson) to determine their geometry type, attribute fields, and attribute structure;

[0146] b. Generate the target table name according to the vector file name + business semantics (e.g., adm_village_2023);

[0147] c. If the table does not exist, it will be automatically created based on the field definitions (including the geom geometry field).

[0148] d. If the table already exists, it can be configured as "append" or "overwrite" mode;

[0149] e. Record audit fields such as inbound time, source path, and quality inspection report ID;

[0150] (2) Raster data (spatial data): archived to object storage (OBS / MinIO)

[0151] a. Package or upload the original raster file (.tiff) and its necessary ancillary files (.ovr, .aux, .xml) separately to OBS / MinIO;

[0152] b. Store the upload path (Path) of the raster file according to the predefined directory template, as well as the additional standard metadata (coordinate system crs, spatial range space, time range time, attribute information attr), and write it into the metadata table of the PostgreSQL database.

[0153] (3) Non-spatial data: Text and images are archived to object storage (OBS / MinIO)

[0154] Upload the file directly to OBS / MinIO and record its upload path. Store the upload path of text and image files according to the predefined directory template and write it into the PostgreSQL database metadata table.

[0155] Step 5: Visualization of Spatiotemporal Data Packages

[0156] Based on the spatiotemporal data packets completed in section 2.10, spatial data is uniformly published as standardized OGC services (WMS / WFS / WCS / WMTS) through GeoServer for front-end visualization; while non-spatial data is directly provided with corresponding online links, which can be opened and viewed directly through the front-end.

[0157] Example 2:

[0158] Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. The device can be a server, workstation, cloud computing node, or server cluster used to execute the spatiotemporal data access service of this invention.

[0159] like Figure 3As shown, the computer device includes: a processor, memory, a network interface, an input / output interface, and a system bus.

[0160] The system bus is used to enable communication between these components.

[0161] The processor is the control center of a computer device, connecting various parts of the device through various interfaces and lines. The processor 601 can be a general-purpose processor (CPU), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices. In this embodiment of the invention, given that spatiotemporal data processing involves a large number of geometric operations (such as JTS topology checking) and file I / O operations, the processor 601 is preferably a multi-core processor with high floating-point arithmetic capabilities, or a processor that works in conjunction with a graphics processing unit (GPU) for parallel accelerated computation.

[0162] The memory is used to store computer programs and data. The memory may include high-speed random access memory (RAM), and may also include non-volatile memory, such as at least one disk storage device, flash memory device, universal flash storage (UFS), etc. In this embodiment, the memory mainly stores:

[0163] Operating system: such as Linux, Windows Server, etc., used to manage hardware resources.

[0164] Application module: This refers to the computer program instructions that implement the method described in Embodiment 1. The program includes specific functional modules, such as a directory parsing module (for performing recursive traversal and triple construction), a template construction module (for generating JSON structures), a rule engine module (for running state machines and four-level quality inspection rules), and a data storage module (for splitting and storing vector and non-vector data).

[0165] Temporary data: such as the node queue generated during BFS traversal, the JSON template cache during construction, and intermediate state data during rule validation.

[0166] The network interface is used to enable data communication between computer devices and external devices (such as user terminals, upstream data acquisition devices, external object storage systems like MinIO / OBS, and external databases like PostgreSQL). In this invention, massive amounts of spatiotemporal data packets (ZIP / Folder) are received and loaded into memory through this network interface.

[0167] The input / output interfaces are used to connect peripherals such as monitors, mice, and keyboards, allowing administrators to perform visual operations such as rule configuration (e.g., writing regular expressions and setting EPSG codes).

[0168] Specifically, the processor implements the method of Embodiment 1 by reading and executing computer program instructions stored in memory.

[0169] Furthermore, this embodiment also provides a computer-readable storage medium. (See reference) Figure 4 The storage medium may be a magnetic random access memory (FRAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory, a magnetic surface memory, an optical disc, or an optical disc read-only memory (CD-ROM), etc.; or it may be a device that includes one or any combination of the above-mentioned memories.

[0170] The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the various steps of the spatiotemporal data dynamic standardization access method based on a configurable rule engine as detailed in Embodiment 1 above.

[0171] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for dynamic standardized access to spatiotemporal data, characterized in that, Includes the following steps: Step S1, Data packet structure parsing: Recursively traverse the received spatiotemporal data packets, parse the directory node structure, identify the node types, and extract the metadata information of the spatial data layer; the node types include at least folders, text, images, and spatial data layers; Step S2, Template Construction: Based on parsing the directory node structure and extracting metadata information, a hierarchical spatiotemporal data template is constructed. The template maps the logical structure of the spatiotemporal data packet in a structured data format. Step S3, rule configuration: Based on the spatiotemporal data template, configure a multi-level quality inspection rule base; The multi-level quality inspection rule base covers at least four dimensions of verification rules: directory structure, layer information, geometric features, and attribute fields. Step S4, Cascaded Quality Inspection and Data Entry: Using the rule engine, the rules in the multi-level quality inspection rule base are executed according to the preset cascaded control logic to verify the spatiotemporal data packet; When the verification passes, the data is split and written to the corresponding storage system according to the data type.

2. The method according to claim 1, characterized in that, In step S1, the extraction of metadata information of the spatial data layer includes: identifying spatial data files using a geospatial data processing library, aggregating related files with the same filename but different suffixes into a single logical layer node; and extracting the coordinate reference system, geometric type, spatial extent, time range, and attribute table structure information of the logical layer node.

3. The method according to claim 1, characterized in that, In step S2, the spatiotemporal data template adopts JSON format; the node objects in the template include path, name, and type fields; for nodes of type spatial data layer, the template also includes geometry type field, coordinate system field, spatiotemporal range field, and attribute list field.

4. The method according to claim 1, characterized in that, In step S3, the multi-level quality inspection rule base specifically includes: Level 1 rules, used to verify the naming standardization of directories and files, the correctness of node types and structural integrity, and the configuration of file content; Level 2 rules, used to verify coordinate system consistency, raster resolution consistency, and band consistency; Level 3 rules, used to verify geometric type consistency and spatial topological relationships, including self-intersection, multi-component, and overlap checks; and Level 4 rules, used to verify the name, type, length, value range, and non-empty constraints of attribute fields.

5. The method according to claim 4, characterized in that, In step S4, the cascading control logic is implemented based on a state machine mechanism: a data processing state machine is constructed, which includes a directory check state, a layer check state, a geometry check state, and an attribute check state; the state machine performs checks sequentially according to the order of the first to fourth level rules; the state transition is triggered to the next check state only when all rule checks in the current state pass; if any state check fails, the process is terminated and an error report is output.

6. The method according to claim 1, characterized in that, In step S1, the spatiotemporal data packet is traversed using a breadth-first search algorithm, and the nodes generated by the traversal are stored as node triples containing path, name, and type.

7. The method according to claim 1, characterized in that, In step S4, the process of splitting and writing data into the corresponding storage system includes: identifying the data type; if it is vector data, then dynamically creating or matching a data table in the relational database according to the attribute table structure information in the spatiotemporal data template, and storing the geometric information after converting it into a spatial format; if it is raster data or non-spatial data, then writing its file entity into the object storage system, and recording the file's metadata information and storage path in the relational database.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

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